Talent in Distressed Firms: Investigating the Labor Costs of Financial Distress

TLDR: In this paper, the importance of skilled labor and the inalienability of human capital expose firms to the risk of losing talent in critical times using Swedish micro-data, and firms lose workers with the highest cognitive and non-cognitive skills as they approach bankruptcy.

Abstract: The importance of skilled labor and the inalienability of human capital expose firms to the risk of losing talent in critical times Using Swedish micro-data, we document that firms lose workers with the highest cognitive and noncognitive skills as they approach bankruptcy In a quasi-experiment, we confirm that financial distress is driving these results: following a negative export shock caused by exogenous currency movements, talent abandons the firm, but only if the exporter is highly leveraged Consistent with talent dependence being associated with higher labor costs of financial distress, firms that rely more on talent have more conservative capital structures

Figures

Figure 2. Evolution of the talent wage premium, 1998 to 2011. The figure shows the evolution of the talent wage premium in Sweden between 1998 and 2011. The sample is constructed as follows. The underlying sample consists of workers in Swedish limited liability firms; we focus on firms with nonmissing information on assets, at least five employees, at least five military test-takers in the first year that the firm enters the sample, and at least five consecutive years of data. Furthermore, we consider all individuals who took military enlistment tests. We estimate the regression model Ln(Wage)i,t = αt Ti,t + X ′β. Ln(Wage) is the natural logarithm of the labor income obtained by an individual from the main employer in a given year. The matrix X includes the following fixed effects: worker age × year, industry, years of education, and Talent (economywide), where Talent (economy-wide) is a dummy variable that takes the value of 1 if a given worker is in the top 5% (alternatively, top 50%) of the skill distribution in the economy in a given year, and skill is measured using the combined cognitive and noncognitive military test scores. T is Talent (economy-wide) interacted with year dummies. The coefficients αt , plotted in the figure below, denote the talent wage premium in a given year relative to that in 1998.

Figure 1. Evolution of labor force in firms approaching bankruptcy. This figure shows the average share of workers leaving and joining bankruptcy and nonbankruptcy firms in a given year. The timing is relative to the year a firm files for bankruptcy (t0) and to the matching year (t – 5). The sample construction is discussed in detail in Section I.

Figure 3. Talent leaving and joining bankruptcy and nonbankruptcy firms. This figure shows the share of top talent (as a fraction of male workers with information on cognitive and noncognitive scores employed in the firm in a given year) leaving and joining bankruptcy and nonbankruptcy firms. Top talent workers are those who lie in the top 5% of the distribution of the sum of cognitive and noncognitive skill scores within the firm in a given year. The timing is relative to the year a firm files for bankruptcy (t0) and to the matching year (t – 5). Sample construction and variable definitions are discussed in detail in Section I.

Table III—Continued

Table XI, Panel A, reports the results. In column (1), we regress Leverage on our firm talent measures and year fixed effects, while in column (2) we include additional controls. The results confirm that the average level of talent in a firm’s labor force is an important determinant of capital structure decisions. In both columns, leverage is negatively correlated with the Average skills of a firm. A one-standard-deviation increase in a firm’s Average skills is associated with a 1.1 percentage point lower leverage ratio (column (2)). Relative to the average level of leverage in the sample (13.3%), this is 8.5% lower leverage than in the average firm. For comparison, a one-standard-deviation increase in Tangibility, Profitability, Ln(Assets), and Firm age is associated with 9.6, −2.5, −0.4, and −0.9 percentage point changes, respectively, in leverage. The estimate associated with Average skills is thus larger than the effect of a one-

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LBS Research Online
R P Baghai, R C Silva, V Thell and V Vig
Talent in Distressed Firms: Investigating the Labor Costs of Financial Distress
Article
This version is available in the LBS Research Online repository:
https://lbsresearch.london.edu/
id/eprint/2022/
Baghai, R P, Silva, R C, Thell, V and Vig, V
(2021)
Talent in Distressed Firms: Investigating the Labor Costs of Financial Distress.
Journal of Finance.
ISSN 0022-1082
(In Press)
DOI: https://doi.org/10.1111/jofi.13077
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THE JOURNAL OF FINANCE
•
VOL. , NO. 0
•
SEPTEMBER 2021
Talent in Distressed Firms: Investigating the
Labor Costs of Financial Distress
RAMIN P. BAGHAI, RUI C. SILVA, VIKTOR THELL, and VIKRANT VIG
ABSTRACT
The importance of skilled labor and the inalienability of human capital expose firms
to the risk of losing talent at critical times. Using Swedish microdata, we document
that firms lose workers with the highest cognitive and noncognitive skills as they ap-
proach bankruptcy. In a quasi-experiment, we confirm that financial distress drives
these results: following a negative export shock caused by exogenous currency move-
ments, talent abandons the firm, but only if the exporter is highly leveraged. Con-
sistent with talent dependence being associated with higher labor costs of financial
distress, firms that rely more on talent have more conservative capital structures.
“For embattled employees of RadioShack, Wet Seal and other companies
facing bankruptcy, the time to find a new job is long before the company
goes under. […] ‘The best time to find a job, is when you have a job,’ says
Ramin P. Baghai is with the Stockholm School of Economics. Rui C. Silva is with the Nova
School of Business and Economics. Viktor Thell is with Finansinspektionen (Sweden’s financial
supervisory authority). Vikrant Vig is with the London Business School and the Kellogg S chool
of Management at Northwestern University. We thank the Editor (Stefan Nagel), two anonymous
referees, Ashwini Agrawal, Xavier Giroud, David Matsa, Gordon Philips, Fabiano Schivardi, Amit
Seru, Henri Servaes, Elena Simintzi, Martin Strieborny, Geoffrey Tate, Luigi Zingales, and semi-
nar and conference participants at the CSEF-EIEF-SITE conference on Labor and Finance (2015),
Finance, Organizations and Markets Conference (2015), Spanish Economic Association meetings
(2015), SHOF PhD conference (2015), SFS Finance Cavalcade (2016), 13th Annual Conference in
Financial Economics Research—IDC, Israel (2016), EFA (2016), Adam Smith Workshops (2017),
UNC/Duke Corporate Finance Conference (2017), NBER Corporate Finance Summer Institute
(2017), Banco de Mexico, Bocconi University, Faculdade de Economia do Porto, London Business
School, Lund University School of Economics and Management, Southampton Business School,
Stockholm School of Economics (Swedish House of Finance), and University of Texas at Dallas.
We thank Johan Wall at Statistics Sweden for his help with the administrative data. We are
grateful for financial support from the Deloitte Institute for Innovation and Entrepreneurship,
the Research and Materials Development Fund at the London Business School, and the Hans Dal-
borg foundation. Thell gratefully acknowledges financial support from the Swedish Bank Research
Foundation, as well as the Jan Wallander and Tom Hedelius Foundation. This project has also re-
ceived funding from the European Research Council (ERC) under the European Union’s Horizon
2020 research and innovation program (grant agreement No. 679747). We have read The Journal
of Finance disclosure policy and have no conflicts of interest to disclose.
Correspondence: Ramin P. Baghai, Stockholm School of Economics, Department of Finance, Box
6501, SE-113 83 Stockholm, Sweden; Phone: +46 8 736 9296; e-mail: ramin.baghai@hhs.se.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial
License, which permits use, distribution and reproduction in any medium, provided the original
work is properly cited and is not used for commercial purposes.
DOI: 10.1111/jofi.13077
© 2021 The Authors. The Journal of Finance published by Wiley Periodicals LLC on behalf of
American Finance Association
1

2 The Journal of Finance
®
Tim Sackett, president of HRU Technical Resources, an information tech-
nology and engineering staffing firm in Lansing, Mich. ‘If you aren’t going
to wait around, it’s best to leave early. Outside companies know the best
talent leaves, or gets recruited the quickest, so if you’re the last one to
jump ship, most people will believe you’re mediocre talent.’”
“When should workers at troubled companies jump ship?” by Quentin
Fottrell, MarketWatch, February 5, 2015.
E
VER SINCE MODIGLIANI AND MILLER’S (1958) famous irrelevance theo-
rem, financial economists have devoted considerable effort to understand the
nature of the frictions that affect firms’ financial choices. Although there is a
consensus that a firm’s financial structure matters and has real effects, the
determinants of a firm’s capital structure are still under investigation. One
prominent theory—the trade-off theory of capital structure—contrasts the ad-
vantages of debt (such as the interest tax shield) with the disadvantages of
high leverage (the costs of financial distress). In theory, the costs are under-
stood to include both direct costs (e.g., legal and advisory fees typically in-
curred during bankruptcy) and indirect costs (e.g., loss of customers, suppliers,
employees). However, while the notion of these costs is clear theoretically, em-
pirically identifying various channels has proven to be difficult.
In this paper, we examine how the onset of financial distress affects firms’
ability to retain highly s killed labor (“talent”) in the organization. A reduced
ability of financially distressed firms to retain such workers may be viewed as
a cost of financial distress. This notion is not new. The property rights view pi-
oneered by Grossman and Hart (1986) and Hart and Moore (1990) provides a
framework for analyzing how the inalienability of human capital affects firms’
financing capacity. Essentially, human capital introduces contractual incom-
pleteness that stems from the fact that firms do not own human capital—
workers do. A recent survey of business professionals suggests that this is
not merely a theoretical possibility. Specifically, “talent and skill shortages”
were identified as the second most important risk facing modern organiza-
tions, topped only by the risk of “loss of customers” and ranking above others
such as “changing legislation” (Lloyds (2011)).
1
Whether a firm’s top talent is the first to desert the proverbial sinking ship is
not a priori obvious. Although a liquid labor market for highly skilled workers
could result in such workers exiting first, it might also make them more pa-
tient because the cost of staying with the firm may be lower (e.g., lower wage
discounts and shorter unemployment spells). To the extent that high-talent
workers are employed in more strategic roles, this would also give them an in-
formational advantage that allows them to gauge the severity of the difficulties
facing the firm. Other factors, such as reputational damage (e.g., attribution of
1
Anecdotal evidence, such as the Saatchi and Saatchi case (e.g., Rajan and Zingales (2000)),
also supports this view. When U.S. fund managers who owned 30% of Saatchi and Saatchi vetoed
the award of a generous compensation package to the firm’s chairman Maurice Saatchi, he and his
brother Charles left the firm, taking with them several key senior executives and accounts.

Talent in Distressed Firms 3
blame), could also affect their decision. This theoretical ambiguity that arises
from various economic forces makes for an interesting empirical investigation.
Several challenges must be overcome in attempting to answer such a ques-
tion. First and foremost, an in-depth analysis of the labor force in financially
distressed firms requires detailed, microlevel data on individual characteris-
tics, job nature, and reasons for departure (voluntary or involuntary), among
other factors. Data of such granularity are not typically available. The empir-
ical hurdles are further compounded by the measurement question of how to
define and measure talent. Since human capital is multidimensional, this is
not straightforward. Finally, one needs a suitable approach to gauge whether
the distress experienced by a firm is financial or economic. This latter dis-
tinction is critical because it is the cost of financial distress that matters for
financial policy.
In this paper, we employ microlevel data from Sweden to overcome these
challenges. Our employee-employer matched data set contains detailed infor-
mation on firm characteristics, as well as individual employee characteristics
such as cognitive and noncognitive skills, age, gender, education, employment
history, and compensation. These data allow us to paint a comprehensive pic-
ture of the evolution of the labor force in firms approaching financial distress.
2
The data set also allows us to create meaningful proxies for talent. We de-
fine and measure talent as a set of cognitive and noncognitive abilities that are
generally applicable across tasks and jobs. Although human capital is multi-
faceted, cognitive and noncognitive skills are closest to the innate concept of
talent that we are attempting to capture.
3
Prior studies show that cognitive and noncognitive skills are important de-
terminants of education and labor market outcomes (e.g., Heckman, Stixrud,
and Urzua (2006), Lindqvist and Vestman (2011)). Such skills are also closely
associated with firm productivity and value creation (e.g., Abowd et al. (2005)).
Employees with high cognitive and noncognitive skills may be particularly in-
dispensable during critical times, such as financial distress, when firms face
unique challenges. A firm might need to implement new and—compared to
its usual modus operandi—unconventional approaches that high-talent work-
ers may find easiest to adapt to and master. The reliance of firms on, and the
risk of losing, workers with these skills, which are portable across firms and
2
We discuss the external validity of our results in Section IV and in Section II of the Internet
Appendix.TheInternet Appendix is available in the online version of this article on The Journal
of Finance website.
3
Other forms of and proxies for human capital may also be important. However, we believe that
cognitive and noncognitive skills are the most accurate proxy available to study the type of labor
cost of financial distress that is of interest in this paper, which focuses on the risk of losing work-
ers whose abilities are widely applicable and sought after in the economy. Moreover, measurement
issues hinder the interpretation of proxies for other dimensions of human capital. For example,
long tenure in the firm may indicate the existence of valuable firm-specific human capital. How-
ever, workers with long tenure may also be “legacy” workers who are apathetic, unmotivated, and
resistant to change. Another example is education. As pointed out by Philippon and Reshef (2012),
there is significant variation in human capital within similar educational groups, and the skills
associated with any particular level of education may change over time.

4 The Journal of Finance
®
generally valuable in the economy, can therefore expose firms to a type of
“fragility” that originates in the characteristics of its workforce.
We begin by investigating whether high-talent employees are prone to leav-
ing firms that are approaching financial distress. Our main finding is that
firms that become financially distressed do indeed experience a significant loss
of talent. Workers with the highest cognitive and noncognitive skills are 65%
more likely to abandon the firm as it approaches distress, relative to the av-
erage worker. Further, we find that the intake of talent in distressed firms
does not increase commensurably. Given the importance of talent for firm pro-
ductivity and value, the fact that high-talent workers abandon firms that are
approaching bankruptcy can be seen as a labor cost of financial distress.
In our study, it is critical to separate demand- and supply-side factors that
lead to a change in the labor composition of distressed firms. For instance, a
lower reliance on talent may be the optimal strategy of a profit-maximizing
firm that is experiencing financial distress. Information on which departures
are voluntary and which are forced (i.e., firing) is rarely coded in any data set.
Although we do not have access to such information, we use two approaches to
identify voluntary departures. Under the first approach, we examine whether
an employee who leaves a firm is subsequently unemployed. Our conjecture is
that forced departures would tend to be associated with unemployment, while
voluntary departures would be less likely to result in unemployment. We find
no evidence of firms firing high-talent workers at an increased rate during
financial distress.
Our second strategy exploits a unique institutional feature of labor laws in
Sweden to separate voluntary from involuntary turnover. Firms with 11 or
more employees are required by law to follow a last-in-first-out (LIFO) rule
when laying workers off.
4
Because we know employees’ joining date, we can
determine whether job separations follow the LIFO rule. Deviations from this
rule provide us a proxy for voluntary departures. We find that high-talent em-
ployees are more likely to leave voluntarily—in effect, “jumping the queue”
and leaving earlier than their LIFO order should dictate. Taken together, our
results point to firms’ top talent voluntarily “abandoning the sinking ship” in
times of financial distress.
After establishing that we are indeed documenting voluntary rather than
involuntary departures by highly skilled employees, we conduct a test aimed
at empirically separating financial distress from economic distress. That is,
we address the question: Does top talent leave because the firm ceases to
be economically viable or because the firm is financially distressed? To an-
swer this question, we consider a sample of Swedish firms exporting to dif-
ferent countries. The idea underlying the test is that a large, exogenous de-
crease in the value of exports due to unfavorable exchange rate movements
is likely to be detrimental to all exporting firms, but the likelihood of finan-
cial distress will increase more for highly levered exporters. This allows us to
4
Sections I.B and III.C of the Internet Appendix discuss the Swedish LIFO regulations and
their impact on firms’ human resources policies in more detail.

Frequently asked questions

Q1. What have the authors contributed in "Talent in distressed firms: investigating the labor costs of financial distress" ?

In this paper, the authors analyzed the evolution of the labor force composition as firms approach bankruptcy and found that firms that rely to a larger extent on talent face higher costs of financial distress and may therefore choose to operate with lower leverage. 

Q2. What are the characteristics of a person’s cognitive and noncognitive skills?

Noncognitive skills refer to personality, social, and emotional traits, such as empathy, sociability, conscientiousness, and perseverance. 

Q3. What are the types of activities pursued by the financial firms included in the sample?

Examples of activities pursued by the financial firms included in the sample are financial leasing, investments, private equity, venture capital, brokerage services, and financial advisors. 

Q4. What is the main concern with the tests that exploit exchange rate movements in different currencies?

One potential concern with the tests that exploit exchange rate movements in different currencies in addition to differences in ex ante capital structures is that firms with different levels of leverage and different export activity may differ along other dimensions. 

Q5. What is the effect of a shock on the likelihood of top talent leaving a firm?

The authors find that following a large negative export shock, top talent in highly leveraged firms (compared to such talent in low-leverage firms experiencing the shock) are significantly more likely to leave. 

Q6. How do the authors determine the probability of a worker leaving a firm?

In column (2), the authors show that workers with high talent have a 4.2 percentage point higher probability of leaving the firm as it approaches bankruptcy than less skilled workers. 

Q7. How long does it take to determine whether a worker leaves a firm?

The authors require two years of consecutive data to determine whether a worker leaves a firm (see Section I.C), and hence the sample ends in 2010. 

Q8. How do the authors determine the probability that top talent workers leave a firm?

The authors formally test whether proximity to bankruptcy is correlated with an increase in the probability that top talent workers leave the firm by estimating a linear probability model. 

Q9. How much more likely are workers with the highest cognitive and noncognitive skills to leave ?

Workers with the highest cognitive and noncognitive skills are 65% more likely to abandon the firm as it approaches distress, relative to the average worker.